Segmentation is a known method of editing images and videos, used primarily for extracting and inserting an object from one image into another, and/or masking off certain areas of an image while performing adjustments. Segmentation of medical images is a common, but time consuming, task in the field of treatment planning. Segmenting targets and organs-at-risk is critical in treatment planning applications because the planning process depends, inter alia, on the accuracy of segmentation for the quality of its results.
Currently, there are many segmentation tools available that range from completely manual to fully automated. For purely manual segmentation methods, the user typically “outlines” or “paintbrushes” the structure. While this method gives the user complete control over the result, it is a very time consuming and tedious task. Additionally, the manual methods can produce large variations between different users. In that regard, automating the segmentation is very attractive in theory. However, automatic methods often relinquish significant control of the results from the user and often fail to produce correct or even satisfactory results. This forces the user to manually correct the results, if such corrections are even possible, which can be as time consuming as manually segmenting the structure from the start. If automatic methods provide any control of the end result, it is often via settings that have a very indirect effect on the result, making it difficult for the user to understand how to best adjust all the settings.
Semi-automatic methods are more realistic than fully automatic methods and quicker than manual methods. These methods greatly reduce the segmentation time while giving the user more direct control of the result. One family of such methods is the “scribble” segmentation method where the user annotates sonic pixels with a label, such as foreground or background, and the computer then determines the best labeling of all unlabeled pixels according to some set criteria. If the result is not satisfactory, the user labels additional pixels until the desired result is obtained.
What is needed is an improved semi-automatic method and system for segmenting images that has the advantages of an automatic method, while allowing users to have more direct control of the result.